• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.340-351
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• 1988

Up to now, hybrid torque/position control of robot manipulator have been researched under the assumption that link mass and/or load are known. This paper proposes a torque and position control method under unknown mass of links or load of a robot manipulator and the method extend control in joint space to control in operational space. In the method, known parameters are used to estimate unknown parameter. We illustrate the theory with some simulations and show that the result is effective.

• International Journal of Reliability and Applications
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• v.15 no.1
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• pp.65-76
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• 2014

In this article, a new model based on Lomax distribution is introduced. This new model is both useful and practical in areas such as economic, reliability and life testing. Some statistical properties of this model are presented including moments, hazard rate, reversed hazard rate, mean residual life and mean inactivity time functions, among others. It is also shown that the distributions of the new model are ordered with respect to the strongest likelihood ratio ordering. The method of moment and maximum likelihood estimation are used to estimates the unknown parameters. Simulation is utilized to calculate the unknown shape parameter and to study its properties. Finally, to illustrate the concepts, the appropriateness of the new model for real data sets are included.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.7
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• pp.72-78
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• 2008

The line-of-sight(LOS) stabilization system is a precision electro-mechanical gimbals assembly for suppressing vibration due to its environment and tracking the target in a desired direction. This paper describes the design of gimbals system to reject the disturbance and to improve stabilization. The controller consists of a DSP with transducer and actuator interfaces. Unknown parameters of the gimbals are estimated by the signal compression method. The cross-correlation coefficient between the impulse response from the assumed model and the one from model of the gimbals is used to obtain the better estimation. The quasi-impulse response through linear element included in the gimbals could be obtained by the signal compression method. The unknown parameter of the linear element could be estimated as comparing the bode plots for impulse response from gimbals with them from model's response.

• Journal of the Korea Society for Simulation
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• v.9 no.4
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• pp.1-10
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• 2000

One of the primary goals of the simulation experiments is to understand the overall system behavior and to analyze the system, ultimately to optimize the system. Optimizing the system includes determining the optimum condition of the system parameters of interest. This paper is concerned with the simulation methodology for estimating the unknown objective function for the system of interest and optimizing the system with respect to the controllable factors. In the process of estimating the unknown objective function, which is assumed to be a second order spline function, we use common random numbers for different set of the controllable factors resulting in more accurate parameter estimation for the objective function. We will show some mathematical result for the benefit of using common random numbers.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• ETRI Journal
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• v.33 no.2
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• pp.283-286
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• 2011

Path loss plays fundamental roles in system design, spectrum management, and performance evaluation. The traditional path loss model has a slight inconvenience; it depends on the unknown distance. In this letter, we explore the probability distribution function (PDF) of path loss in an indoor office environment by randomizing out the distance variable. It is shown that the resulting PDF is not Gaussian-like but is skewed to the right, and both the PDF and the moments are related to the size of the office instead of the unknown distance. To be specific, we incorporate the IEEE 802.15.4a channel parameters into our model and tabulate the cumulative distribution function with respect to different room sizes. Through a simple example, we show how our model helps a cognitive spectrum user to infer path loss information of primary users without necessarily knowing their transmitter-receiver distance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.315-322
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• 2004

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.476-482
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• 2006

Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.737-741
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• 1987

The induction motor is represented by nonlinear equations whose parameters are changing with respect to the slip-frequency, temperature, etc. The slip-frequency is effected by unknown load which is difficult to estimate on-line. Astable vector controller is designed with direct MRAC to improve the quality of the transient response. The unknown load is considered in this speed controller design, and tested by simulation. Also a flux controller is designed and tested to reduce the audible noise in this paper.